Precision and recall

Key metrics in evaluating the performance of classification models, precision measures the ratio of correctly predicted positive observations to the total predicted positives, while recall measures the ratio of correctly predicted positive observations to all the observations in the actual class. Precision provides insight into the accuracy of the positive predictions made by the model, whereas recall indicates the model's ability to identify all relevant instances within a dataset. These metrics are crucial for data scientists and machine learning practitioners aiming to optimize and assess the effectiveness of their classification algorithms.